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NEW QUESTION # 56
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
NEW QUESTION # 57
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Microsoft 365 Copilot connectors are used to bring external line-of-business and third-party content into the Microsoft 365 ecosystem so Copilot can use that information in supported experiences. Connectors help make data from systems outside Microsoft 365 discoverable and usable through Microsoft Graph-based indexing, while still respecting permissions and governance controls. This allows users to ask natural-language questions and receive answers grounded in business data from connected systems. Image and video analysis is associated with vision services, not Copilot connectors. Security incident summaries are aligned with Microsoft Security Copilot. Suggested email replies are a Microsoft 365 Copilot productivity feature in Outlook, not the primary purpose of connectors.
NEW QUESTION # 58
Your company sells hiking and camping gear online. You need a generative AI solution that can interact with customers and ask questions about their needs. What should you include in the solution?
Answer: A
NEW QUESTION # 59
- For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
Answer Area
* A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
answer: No
* Content filtering and responsible AI safeguards help a generative AI model generate safe and inoffensive content. Answer: Yes
* A generative AI model always produces fair and unbiased results when the training data has been properly prepared and reviewed for fairness. Answer: No
* No - A larger training dataset can improve coverage and fluency, but it does not guarantee factual accuracy. Generative models can still hallucinate, mix concepts, or produce plausible-but-incorrect statements because they generate likely text rather than verifying truth. This is why solution designs commonly add grounding/retrieval, validation, and human review for high-stakes outputs.
* Yes - Content filtering and Responsible AI controls are specifically used to reduce harmful, unsafe, or policy-violating outputs . In practice, safeguards include input/output filters, safety classifiers, and governance controls that help enforce safety policies and minimize offensive content. These controls don't make outputs "perfect," but they materially reduce risk and are a standard part of production AI deployments.
* No - Even with careful data preparation and fairness reviews, models can still produce biased outcomes due to residual bias in data, label/measurement issues, deployment context, and shifting real- world distributions. "Always fair and unbiased" is an absolute claim that is not achievable in real systems; fairness is managed through continuous evaluation, monitoring, and mitigations-not assumed as guaranteed.
NEW QUESTION # 60
Your company has an AI solution that uses a prebuilt Azure OpenAI model to generate content. You need to reduce the cost of the solution while minimizing the impact on the quality of the generated output. Which two actions should you perform? (Select TWO.) NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
To reduce Azure OpenAI costs with minimal quality loss, you target the biggest cost drivers: token usage and model price per token (or throughput unit). C (Optimize the prompts) is a best practice because shorter, clearer prompts reduce unnecessary input tokens and often reduce output length by tightening instructions and formatting. Prompt optimization can preserve or even improve quality by removing ambiguity, adding constraints, and using compact context (for example, only the most relevant grounding passages). Lower token consumption directly lowers cost while maintaining response usefulness.
D (Switch to an alternate model) is also effective because different models have different price/performance tradeoffs. Moving from a premium model to a more cost-efficient model (or a smaller variant) can significantly reduce spend. You can minimize quality impact by validating outputs on representative scenarios and using a tiered approach (cheap model by default, expensive model only for complex cases).
The other options are less aligned to the goal. A (Fine-tune) typically increases cost (training and ongoing evaluation) and is not the first-line cost reducer. B (Content moderation) is primarily a safety control; it can add overhead and doesn't directly reduce token costs. E (Decrease hosting hours) applies to capacity-based hosting scenarios, but the question states a prebuilt Azure OpenAI model for content generation-cost reduction is best achieved by prompt/token optimization and selecting the right model.
NEW QUESTION # 61
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